1. Schema Matching and Query Rewriting in Ontology-based Data Integration Zdeňka Linková linkova@cs.cas.cz ICS AS CR Advisor: Július Štuller

2. Acknowledgement This work was supported by the project 1ET100300419 of the Program Information Society (of the Thematic Program II of the National Research Program of the Czech Republic)Intelligent Models, Algorithms, Methods and Tools for the Semantic Web Realization.

3. Outline of presentation Introduction Virtual data integration Ontology based system Matching in the system Mapping in the system Query rewriting Conclusion

4. Introduction Todays world is a world of information Web data use expansion Need of efficient information processing => Semantic web idea (XML, RDF, ontologies) Many data providers, working with distributed data Need of data integration => Semantic web data integration

5. Virtual data integration Data stays physically stored in original sources Data integration provides an integrated view over distributed data Virtual data integration: Schema matching Schema mapping Query processing

6. Ontology-based system Sources: Semantic web data (local and global)... RDF/XML Available ontologies for the sources... OWL Task input: sources S i and ontologies O j Use of ontologies: Source ontologies and global ontology for provided integrated data To do matching To describe mapping To query rewriting

7. Relationships in the system Schema matching – process of searching schema correspondences Schema mapping – description of found schema correspondences, i.e. definition of relation, rule, formula etc. (1-1 rules, use of views, LAV and GAV approaches...) Consider correspondences kinds: Is-a hierarchical relationship, Equivalence Disjointness

8. Matching and mapping in the system For description of found correspondences in mapping, OWL ontologies and its features are used: rdfs:subClassOf for and owl:equivalentClass for owl:disjointWith for => Ontology O I... ontology of the integration system... contains mapping in the system How is O I obtained?

9. Matching and mapping in the system Shared ontology case: All data are described in only one (shared) ontology – in that data relationships are described => no need to search somewhere else General case – shared ontology not available: Local ontologies describing data in the local sources Need to obtain shared ontology => Integration local sources ontologies The task is transformed to the ontology merging task Available tools developed when solving this task kind can be employed: Chimaera, PROMPT (Protégé), FCA-MERGE, HCONE (WordNet)

10. Related work on matching Various approaches searching schema correspondences at different levels: Instance – data processing, e.g. domain Terms – string processing, vocabularies use,... Structure – graphs methods applying,... Classical approaches in schema matching and mapping: Estimation from available information (data, structure, external informational sources, …) Candidates selection (meassures, uncertainty,...) Here, the task is solved by merging ontologies: However, in ontology merging, similar principles as mentioned above are used => similar principles are used at different level

11. Querying the integrated data Sources S j contains RDF/XML data Querying using SPARQL language Given guery in global environment... Q G However, data available only in local sources with local environments Task: to rewrite the query to the local environment of the local sources with use of mapping... Q L S i Use of mapping for rewriting

12. Using mapping described in ontology Passing the OWL ontology graph through equivalent or hierarchical relation Using the known OOP rule: a child can substitute its parent For term t: generating set of all possible term rewritings... R(t) End condition: difference in between two passing steps is zero

13. Using mapping described in ontology

14. Simple query processing Simple query – only simple condition on RDF triple For each term t in the query generate set of all possible term rewritings … R(t) Using all R(t) for each term in the query obtain all possible query rewritings … Q L Using local queries Q L on local sources obtain local answers Using reverse rewriting return answer placed in global environment … global answer

15. Simple query rewriting Optimalization: Querying all possible query rewritings in each local source is not effective => Using set of supported terms for each source Obtained from ontology, source schema, source preprocessing… Generating set of all relevant term/query rewritings for each source

16. Complex query processing Complex query – also complex condition on searched RDF triple Complex query is divided into simple queries by dividing complex condition into simple ones Obtained answers corresponding to simple queries must be composed to the answer corresponding to the original (complex) query

17. Conclusion Use of ontologies in virtual data integration: Transformes data integration task to ontology merging task Can bring use of formalism, methods and tools from the other task area Can help in task automatization effort Standardized structure instead of particular project oriented mapping rules bring possibility of reuse of mapping Possibility of expression various terms relations Future plans: experiments with real data

18. Thank for your attention